1

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

df = pd.read_csv("C:/Users/Chhavi Bhadana/Downloads/KCLT.csv")
df.columns

Index(['date', 'actual_mean_temp', 'actual_min_temp', 'actual_max_temp',
       'average_min_temp', 'average_max_temp', 'record_min_temp',
       'record_max_temp', 'record_min_temp_year', 'record_max_temp_year',
       'actual_precipitation', 'average_precipitation',
       'record_precipitation'],
      dtype='object')

2.

x = df['date']
y1 = df['actual_max_temp']
y2 = df['actual_min_temp']
y3 = df['actual_mean_temp']

plt.bar(x, y1, color = 'pink', label = '1 = max_temp')
plt.bar(x, y3, color = 'blue', label = '3 = mean_temp')
plt.bar(x, y2, color = 'yellow', label = '2 = min_temp')
plt.legend()
plt.annotate('min1', xy=(df['date'][y1.idxmin()], y1.min()))
plt.annotate('min2', xy=(df['date'][y2.idxmin()], y2.min()))
plt.annotate('min3', xy = (df['date'][y3.idxmin()], y3.min()))
plt.annotate('max1', xy =(df['date'][y1.idxmax()], y1.max()))
plt.annotate('max2', xy =(df['date'][y2.idxmax()], y2.max()))
plt.annotate('max3', xy =(df['date'][y3.idxmax()], y3.max()))

Text(2015-06-24 00:00:00, 88, 'max3')

3.

df['date'] = pd.to_datetime(df['date'])

df1 = df[df['date'].dt.year == 2014]
x1 =  df1[df1['date'].dt.month == 8]
x1 = x1['actual_mean_temp']
plt.hist(x1)

(array([ 2.,  1.,  4.,  1.,  2.,  1., 10.,  3.,  4.,  3.]),
 array([68. , 69.5, 71. , 72.5, 74. , 75.5, 77. , 78.5, 80. , 81.5, 83. ]),
 <BarContainer object of 10 artists>)

df2 = df[df['date'].dt.year == 2015]
x2 = df2[df2['date'].dt.month == 3]
x2 = x2['actual_mean_temp']
plt.hist(x2)

(array([2., 2., 1., 2., 4., 8., 3., 6., 2., 1.]),
 array([35. , 38.6, 42.2, 45.8, 49.4, 53. , 56.6, 60.2, 63.8, 67.4, 71. ]),
 <BarContainer object of 10 artists>)

4.

df.boxplot(['actual_mean_temp', 'actual_min_temp', 'actual_max_temp'])

<AxesSubplot:>

Observations: None of the the three datasets have outliers The max temprature recorded was 100 The min temprature recorded was approximately 5 The mean of actual_mean_temp is close to the third quartile of actual_min_temp

5.

col1 = df['actual_mean_temp']
col2 = df['actual_max_temp']
corelation = col1.corr(col2)
print(corelation)

0.9731336995939666

The correlaton between actual min temp and actual max temp is positive

6.

a = df['actual_min_temp']
plt.scatter(col1, a, color = 'purple', alpha = 0.5)
plt.xlabel('actual mean temp')
plt.ylabel('acctual min temp')

Text(0, 0.5, 'acctual min temp')

plt.scatter(col1, col2, color = 'g', alpha = 0.5)
plt.xlabel('actual mean temp')
plt.ylabel('acctual max temp')

Text(0, 0.5, 'acctual max temp')

7.

import seaborn as sn
corrMatrix = df.corr()
sn.heatmap(corrMatrix)

<AxesSubplot:>

sn.set_theme(style = 'ticks')
sn.pairplot(df)

<seaborn.axisgrid.PairGrid at 0x1b7111771c8>